A Generalized Approach towards Soft Expert Sets via Neutrosophic Cubic Sets with Applications in Games

Soft Expert Symmetric Group and Its Application in MCDM Problem

Researchers are always inspired to broaden their explorations towards uncertainty theories, owing to their great interest and importance. Soft set theory plays a primary role among all recent uncertainty tools. Though this theory sounds good in all aspects, it has its own limitations due to a lack of experts. The novel idea of a soft expert set was brought up recently to address this issue. This strategy is innovative and inventive in the sense that it utilizes the expertise of numerous specialists. This novel idea inspired us a lot for the development of the present study. This paper introduces the notion of a soft expert symmetric group as a natural generalization of the symmetric group and soft expert set. Several interesting properties of soft expert symmetric groups are studied. Internal and external products of two soft expert symmetric groups and the homomorphism of soft expert symmetric groups are also presented. The application of a soft expert symmetric group in multi-criteria decision-making situations is also given in a lucid manner.

On the similarity measures of N-cubic Pythagorean fuzzy sets using the overlapping ratio

The similarity measures are essential concepts to discuss the closeness between sets. Fuzzy similarity measures and intuitionistic fuzzy similarity measures dealt with the incomplete and inconsistent data more efficiently. With time in decision-making theory, a complex frame of the environment that occurs cannot be specified entirely by these sets. A generalization like the Pythagorean fuzzy set can handle such a situation more efficiently. The applicability of this set attracted the researchers to generalize it into N-Pythagorean, interval-valued N-Pythagorean, and N-cubic Pythagorean sets. For this purpose, first, we define the overlapping ratios of N-interval valued Pythagorean and N-Pythagorean fuzzy sets. In addition, we define similarity measures in these sets. We applied this proposed measure for comparison analysis of plagiarism software.